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Figure 4. Heterospio longissima Ehlers, 1874 in First record of Longosomatidae (Annelida: Polychaeta) from Iceland with a worldwide review of diagnostic characters of the family
Figure 4. Heterospio longissima Ehlers, 1874 sensu Hartman (1965): Specimens from BIOICE samples 2414 and 2474. (A, B) capillary chaetae (cc) and subuluncini (su) of CH13 and CH14; (C) capillary chaetae of CH13; (D–G) detail of distal end of subuluncini of CH11 to CH14; (H) detail of tip of subuluncini without distal appendage from CH14. Scale bars: A, B, 20 µm; C–G, 3 µm; H, 5 µm.
Figure 3. Heterospio longissima Ehlers, 1874 in First record of Longosomatidae (Annelida: Polychaeta) from Iceland with a worldwide review of diagnostic characters of the family
Figure 3. Heterospio longissima Ehlers, 1874 sensu Hartman (1965). Specimen from BIOICE sample 2414. (A–D) Chaetigers 11 to 14. Scale bars: A, 100 µm; B, 150 µm; C, 200 µm; D, 100 µm.
JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database
<p>SUMMARY</p> <p>The <em>JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database</em>, aimed to collect in a single place all publicly available information on performance of CE-marked <em>in vitro</em> diagnostic medical devices (IVDs) as well as <em>in house</em> laboratory-developed devices and related test methods for COVID-19, is here presented. The database, manually curated and regularly updated, has been developed as a follow-up to the Communication from the European Commission “Guidelines on <em>in vitro</em> diagnostic tests and their performance” of 15 April 2020 and is freely accessible at <a href="https://covid-19-diagnostics.jrc.ec.europa.eu/">https://covid-19-diagnostics.jrc.ec.europa.eu/</a>.</p>
Unbiased metagenomic sequencing complements specific routine diagnostic methods and increases chances to detect rare viral strains
<p>Raw Illumina MiSeq data in zipped FASTQ format.</p> <p>Files are named by sample type and time point (weeks after transplantation).</p>
FIGURE 5. Proleptonchus prerectus n in Three new and a known species of the genus Proleptonchus Lordello, 1955 (Nematoda: Leptonchidae) with a diagnostic compendium of the genus
FIGURE 5. Proleptonchus prerectus n. sp. A. & B. Anterior region. C. Anterior end showing amphid. D. Pharyngeal bulb. E. Female genital system. F. Female genital system showing refringent apophyses. G. Female posterior region. H. Prerectal chamber. I. Male posterior region. (Scale bar A – C, E = 10 µm; D, F – I = 20 µm).
FIGURE 7. Proleptonchus japonicus n in Three new and a known species of the genus Proleptonchus Lordello, 1955 (Nematoda: Leptonchidae) with a diagnostic compendium of the genus
FIGURE 7. Proleptonchus japonicus n. sp. A. & B. Anterior region. C. Anterior end showing amphid. D. & E. Pharyngeal bulb. F. Female genital system. G. Vulval region. H. & I. Female posterior region and showing caudal pore. (Scale bar A – C, G = 10 µm; D – F, H, I = 20 µm)
FIGURE 3. Proleptonchus kazirangus n in Three new and a known species of the genus Proleptonchus Lordello, 1955 (Nematoda: Leptonchidae) with a diagnostic compendium of the genus
FIGURE 3. Proleptonchus kazirangus n. sp. A & B. Anterior region. C. Anterior end showing amphid. D & E. Pharyngeal bulb. F. Pharyngeal bulb region showing dorsal and ventral body pores. G. Female genital system. H. Female genital system showing refringent apophyses. I. Vulval region. J. Female posterior region. K. Male posterior region. (Scale bar A – C, I = 10 µm; D – H, J, K = 20 µm)
FIGURE 1 in Three new and a known species of the genus Proleptonchus Lordello, 1955 (Nematoda: Leptonchidae) with a diagnostic compendium of the genus
FIGURE 1. Proleptonchus shamimi Bajaj & Bhatti, 1980 A – C. Anterior region. D. Anterior end showing amphid. E & F. Pharyngeal bulb. G. Vulval region. H-J. Female genital system. K. Female posterior region. (Scale bar A – D, I = 10 µm; E – H, J = 20 µm).
Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging
<p>Fourier transform infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However this ground breaking technology is still in its infancy and its applicability for routine disease diagnosis is, as yet, unproven. In light of this we report on a large study utilising a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm<sup>-1</sup>, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and non-malignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilising high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.</p>
Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen'
<p>Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' consisting of (1) the complete data set of all data analyzed for the project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' ('Data_Brain-IT-Validation-Qmci_for-publication.xlsx'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Fig. 9. Cornucistela serrata, distribution. 1 in New data on diagnostics and distribution of the little-known comb-clawed beetle Cornucistela serrata (Coleoptera: Tenebrionidae: Alleculinae)
Fig. 9. Cornucistela serrata, distribution. 1, Wadi Khumra (holotype); 2, Heith (paratypes); 3, Kushm al-Buway- biyat (paratypes); 4, Quai'iya (specimen collected by Philby).
Fig. 4 in A new species of the genus Ecphylus (Hymenoptera: Braconidae: Doryctinae) from Taiwan, with a diagnostic character previously unknown in the genus
Fig. 4. Behavior of Ecphylus lini sp. nov. A, B, parasitoid male (A) and female (B) on surface of tree after hatching and stretching wings; C–H, newly emerged adults (males) waiting near bark holes for mating attempts with newborns.
Fig. 3 in A new species of the genus Ecphylus (Hymenoptera: Braconidae: Doryctinae) from Taiwan, with a diagnostic character previously unknown in the genus
Fig. 3. Biological properties of Ecphylus lini sp. nov. A, galleries of the host Scolytus japonicus under bark of Zelkova serrata; B, ectoparasitoid larva on the host larva; C, ectoparasitoid cocoon; D, ectoparasitoid pupa; E–H, adult egressing from host gallery via a gnawed hole through the tree bark.
Diagnostic plots of cross-matches between Fermi-LAT (4FGL-DR4) and SRG/eROSITA sources (eRASS:4)
<p>This repository provides diagnostic plots used for visual inspection accompanying the paper "Searching for X-ray counterparts of unassociated Fermi-LAT sources and rotation-powered pulsars with SRG/eROSITA" by Martin G. F. Mayer and Werner Becker, published in Astronomy & Astrophysics in 2024. </p> <p>The attached tarball contains ~ 500 PDF files, one per Fermi source with at least one possible X-ray counterpart. In each file, the top row of panels displays the eROSITA (eRASS:4) X-ray image of the Fermi-LAT uncertainty region in the energy bands 0.2-0.6, 0.6-2.3, and 2.3-5.0 keV, overlaid with the 1 sigma (blue dashed line) and 95% error ellipse of the 4FGL source (blue solid line), and the position of the detected X-ray sources (red). The pixel size is 12 arcsec.</p> <p>For each X-ray source, the bottom panels show the local distribution of optical sources from Gaia DR3 (left), mid-infrared sources from CatWISE2020 (center), as well as SIMBAD entries and radio sources (right) around the respective X-ray source position (error bars). In the left and center panels, the size of the markers is proportional to the source brightness, and their color reflects the photometric colors (with bluer markers indicating bluer colors). In the left panel, pentagonal (triangular) markers correspond to sources with statistically significant (insignificant) proper motion or parallax, circular markers indicate the lack of a complete astrometric solution for a source. </p>
Fig. 1 in First Japanese Records of the Indo-Pacific Scorpionfish (Scorpaenidae) Scorpaenodes corallinus, with a Re-evaluation of Coronal Spines as a Diagnostic Character
Fig. 1. Fresh specimen of Scorpaenodes corallinus (KAUM–I. 58534, 80.0 mm SL, off Tomori, Yoron Island, Amami Islands, Japan).
Fig. 2 in First Japanese Records of the Indo-Pacific Scorpionfish (Scorpaenidae) Scorpaenodes corallinus, with a Re-evaluation of Coronal Spines as a Diagnostic Character
Fig. 2. Underwater photograph of Scorpaenodes corallinus from Japan (KPM-NR 80574, Unanzaki, Aka Island, Kerama Islands, 18 m depth, 26 October 2001, taken by A. Moriyama).
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Fig. 12. Hadruroides Pocock, 1893, diagnostic characters. A–C. Hadruroides geckoi, n in The Genus Hadruroides Pocock, 1893 (Scorpiones: Iuridae), in Peru: New Records and Descriptions of Six New Species
Fig. 12. Hadruroides Pocock, 1893, diagnostic characters. A–C. Hadruroides geckoi, n. sp., paratype ♀ (MHNC). A. Metasomal segment V and telson, lateral aspect. B. Dextral pedipalp chela, ventrointernal aspect. C. Dextral pedipalp chela, external aspect. D. H. geckoi, paratype ♂ (MHNC), sternite VII and metasomal segments I–V, ventral aspect showing pigmentation pattern. E. Hadruroides carinatus Pocock, 1900, ♂ (MHNC), dextral pedipalp chela, ventral aspect. F. H. geckoi, paratype ♂ (MHNC), tergite IV, dorsal aspect showing pigmentation pattern. G. Hadruroides vichayitos, n. sp., paratype ♂ (MHNC), tergite IV, dorsal aspect showing pigmentation pattern. H. H. carinatus, ♂ (MHNC), tergite IV, dorsal aspect showing pigmentation pattern. Scale bars = 1 mm.
Fig. 9. Hadruroides Pocock, 1893, diagnostic characters. A–C. Hadruroides chinchaysuyu, n in The Genus Hadruroides Pocock, 1893 (Scorpiones: Iuridae), in Peru: New Records and Descriptions of Six New Species
Fig. 9. Hadruroides Pocock, 1893, diagnostic characters. A–C. Hadruroides chinchaysuyu, n. sp. A. Paratype ♂ (MHNC), leg III, dextral patella, dorsal aspect. B. Paratype ♀ (MHNC), dextral pedipalp chela, external aspect. C. Paratype ♂ (MHNC), metasomal segments III–V, ventral aspect showing pigmentation pattern. D. Hadruroides maculatus (Thorell, 1876), ♂ (MHNC), leg III, dextral patella, dorsal aspect. E–H. H. chinchaysuyu, holotype ♂ (MHNC), sinistral hemispermatophore. E. Ental aspect. F. Dorsal aspect. G. Ectal aspect. H. Ventral aspect. Scale bars = 1 mm.
Fig. 7. Hadruroides Pocock, 1893, diagnostic characters. A, C. Hadruroides chinchaysuyu, n in The Genus Hadruroides Pocock, 1893 (Scorpiones: Iuridae), in Peru: New Records and Descriptions of Six New Species
Fig. 7. Hadruroides Pocock, 1893, diagnostic characters. A, C. Hadruroides chinchaysuyu, n. sp., paratype ♂ (MHNC). B, D. Hadruroides maculatus (Thorell, 1876), ♂ (MHNC). A, B. Carapace. C, D. Sternite VII and metasomal segment I, ventral aspect. Scale bars = 1 mm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.